Automated Segmentation of the Healed Anterior Cruciate Ligament from T * Relaxometry MRI Scans.

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Abstract

Collagen organization of the anterior cruciate ligament (ACL) can be evaluated using T2 * relaxometry. However, T2 * mapping requires manual image segmentation, which is a time-consuming process and prone to inter- and intra- segmenter variability. Automating segmentation would address these challenges. A model previously trained using Constructive Interference in Steady State (CISS) scans was applied to T2 * segmentation via transfer learning. It was hypothesized that there would be no significant differences in the model’s segmentation performance between T2 * and CISS, structural measures versus ground truth manual segmentation, and reliability versus independent and retest manual segmentation. Transfer learning was conducted using 54 T2 * scans of the ACL. Segmentation performance was assessed with Dice coefficient, precision, and sensitivity, and structurally with T2 * value, volume, sub-volume proportions, and cross-sectional area. Model performance relative to independent manual segmentation and repeated segmentation by the ground truth segmenter (retest) were evaluated on a random subset. Segmentation performance was analyzed with Mann-Whitney U tests, structural measures with Wilcoxon signed-rank tests, and performance relative to manual segmentation with RMANOVA/Tukey tests (α=.05). T2 * segmentation performance was not significantly different from CISS on all measures (p>.35). No significant differences were detected in structural measures (p>.50). Automatic segmentation performed as well as retest on all segmentation measures, while independent segmentations were lower than retest and/or automatic segmentation (p<.023). Structural measures were not significantly different between segmenters. The automatic segmentation model performed as well on the T2 * sequence as on CISS, and outperformed independent manual segmentation while performing as well as retest segmentation. This article is protected by copyright. All rights reserved.This article is protected by copyright. All rights reserved.